A time-frequency channel attention and vectorization network for automatic depression level prediction
Mingyue Niu1,2; Bin Liu1,2; Jianhua Tao1,2,3; Qifei Li1
发表期刊Neurocomputing
2021
期号450页码:208-218
摘要

Physiological studies have illustrated that speech can be used as a biomarker to analyze the severity of depression and different frequency bands of the speech spectrum contribute unequally for depression detection. To this end, we propose a Time-Frequency Attention (TFA) component and combine it with the Squeeze-and-Excitation (SE) component to form our Time-Frequency Channel Attention (TFCA) block for emphasizing those discriminative timestamps, frequency bands and channels. In addition, considering the time-frequency attributes of the data, a Time-Frequency Channel Vectorization (TFCV) block is proposed to vectorize the tensor. Furthermore, we merge the proposed blocks (i.e., TFCA and TFCV blocks) and the two blocks (i.e., Dense block and Transition Layer) of the DenseNet into a unified architecture to form our Time-Frequency Channel Attention and Vectorization (TFCAV) network. In this way, to predict the depression level of an individual, we firstly introduce the sphere embedding normalization method to preprocess the long-term logarithmic amplitude spectrum for maintaining the time-frequency attributes and divide it into segments. Then, these segments are input into the TFCAV network to obtain the depression scores. Finally, the average of scores is taken as the result corresponding to the long-term spectrum. Our method is validated on two challenging databases, i.e., AVEC2013 and AVEC2014 depression databases. The experimental performance illustrates the superiority of the proposed network over some previous methods.

关键词Sphere embedding normalization DenseNet Transition layer Time-frequency channel attention block Time-frequency vectorization block Depression detection
收录类别SCI
七大方向——子方向分类多模态智能
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/44398
专题多模态人工智能系统全国重点实验室_智能交互
通讯作者Bin Liu; Jianhua Tao
作者单位1.National Laboratory of Pattern Recognition, CASIA, Beijing, China
2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
3.CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing, China
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
推荐引用方式
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Mingyue Niu,Bin Liu,Jianhua Tao,et al. A time-frequency channel attention and vectorization network for automatic depression level prediction[J]. Neurocomputing,2021(450):208-218.
APA Mingyue Niu,Bin Liu,Jianhua Tao,&Qifei Li.(2021).A time-frequency channel attention and vectorization network for automatic depression level prediction.Neurocomputing(450),208-218.
MLA Mingyue Niu,et al."A time-frequency channel attention and vectorization network for automatic depression level prediction".Neurocomputing .450(2021):208-218.
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